Generating three dimensional (3-D) information from a stereo image is a significant task in 3-D and other multi-view image processing. It is noted that a real-world point (e.g., a viewed object) projects to a unique pair of corresponding pixels in stereo images. Based on the stereo images, it may be possible to extract or otherwise generate 3-D information from the stereo images corresponding to the same real-world point. Determining the location of a point in the projected stereo images from the subject point generally results in a correspondence problem. Solving the correspondence problem may include generating an estimation of a disparity map.
The difference in the position of the two corresponding points of the stereo images associated with the same real-world image is generally referred to as a disparity. A map of disparity in the projection of multiple real-world points in left and right (i.e., stereo) images may be referred to as a disparity map. Some heretofore techniques to generate a disparity map include local, global, and iterative techniques. However, each of these techniques is not without their own shortcomings. For example, with a local approach, the estimation of the disparity map depends on the intensity values within a finite window and the computational cost is thus low. Conversely, a global approach may use non-local constraints to reduce sensitivity to local regions such as occluded and textureless regions and the computational cost of the global approach is thus high compared to the local approach. Additionally, previous iterative approaches may employ coarse-to-fine techniques that typically operate on an image pyramid where results from the coarser levels are used to define more local search at finer levels. Improving the efficiency of such iterative approaches is therefore important.